The present invention relates to new and critical information discovery, processing, and analysis. In particular, various embodiments of the present invention relate to a system and a method for knowledge pattern search from networked agents, wherein the knowledge pattern search is associated with a pattern-identifying analysis model construction and application and real-time data analysis. Furthermore, various embodiments of the present invention are also related to constructing knowledge patterns identified through data mining and text mining, supervised or unsupervised machine learning, and pattern recognition methods.
One of the disadvantages of using conventional search engines for a computer-networked environment (e.g. data network such as the Internet, intranets, LAN's, and etc.) is that they typically sort documents based on the popularity of documents among linked or relevant documents. The conventional popularity-based relevance ranking in conventional search engines for a computer-networked environment is often based on the assumption of linked documents or databases (e.g. Google's PageRank algorithm is largely based on how many in-coming links a page has), and not based on semantics among the documents or databases. Therefore, it may not satisfy search needs or relevance among pieces of information, if the links among the documents or databases are not available. For example, documents in a typical enterprise among different business categories, which are not cross-linked like in the world wide web, may not show up in search results together coherently, even if there are pieces of information in the contents (e.g. semantics) of the documents which render them to be mutually relevant.
Machine-based understanding of semantics and extracting meaning from the semantics among pieces of information to discover events, patterns, and trends can be a challenging task, which is currently only performed in small scales for a small amount of information. At best, there are a number of extant tools for data and text mining in the advanced search engines such as keyword analysis and tagging. These conventional search engines may employ search assistant and language tools, but only offer suggestions of keywords as a user types a certain term into a search engine. However, these conventional keyword analysis and tagging are unable to provide pattern identifications or predictive capabilities to a user.
Furthermore, there is increasing need to share data mining results and search indexes across multiple organizations and businesses that require analysis of open-source data, which may comprise uncertain, conflicting, partial, and unverified data. Organizations and businesses increasingly comprise culturally and geographically-diverse partners with rapidly changing team members and various organizational structures. Because real-time information present in computer networks, including structured data from databases and unstructured data such as text, is enormous and often distributed among millions of computers around the world, a method to collect relevant data to a centralized location has been devised (e.g. a web crawler), but these methods are generally expensive to implement and maintain.
Therefore, the conventional search engine business is generally expensive to operate and maintain, because computer systems associated with the conventional search engine has to copy and store all the data locally before it can index them. In order to respond to this challenge, more powerful information analysis tools which can quickly extract meaning and intent from an origin of data may be beneficial. It may be even more beneficial, if the data mining results or indexes can be accessed across a data network without leaving local computers, or other origins of localized data.
Because a popular piece information is not usually new or unique, the conventional method of searching information in a computer-networked environment may not be useful for certain types of information discovery applications in which a user seeks new, unique, and/or interesting information which may be not popular or well-known by other users. Searching for new, unique, and/or interesting information regardless of their popularity may enable more accurate predictions for early warnings systems for data anomaly detection, competitive intelligence, and business analysis. Furthermore, utilizing localized data mining results or search indexes in each learning agent (e.g. a local computer, an electronic device connected to a local computer, and etc.) to produce collaborate search returns without moving large amounts of data among different learning agents may also be beneficial.
Therefore, a novel system and a related method, which can discover useful information patterns and data anomalies based on semantical analysis and collaborative search returns of various pieces of disjointed yet new and unique information from multiple information sources (i.e. “learning agents”), may be highly beneficial for users in the field of data anomaly detection, competitive intelligence, and business analysis.